Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations196124
Missing cells2963251
Missing cells (%)63.0%
Duplicate rows1730
Duplicate rows (%)0.9%
Total size in memory35.9 MiB
Average record size in memory192.0 B

Variable types

Numeric5
Categorical4
DateTime8
Text7

Alerts

Dataset has 1730 (0.9%) duplicate rowsDuplicates
assignment_id is highly overall correlated with periodoHigh correlation
nombre_examen is highly overall correlated with particionHigh correlation
nota_final_materia is highly overall correlated with nota_parcialHigh correlation
nota_parcial is highly overall correlated with nota_final_materiaHigh correlation
particion is highly overall correlated with nombre_examenHigh correlation
periodo is highly overall correlated with assignment_idHigh correlation
periodo is highly imbalanced (83.2%)Imbalance
points_possible is highly imbalanced (97.5%)Imbalance
fecha_mesa_epoch has 189114 (96.4%) missing valuesMissing
nombre_examen has 189114 (96.4%) missing valuesMissing
nota_parcial has 189114 (96.4%) missing valuesMissing
assignment_id has 170063 (86.7%) missing valuesMissing
ass_name has 170063 (86.7%) missing valuesMissing
ass_created_at has 170063 (86.7%) missing valuesMissing
ass_due_at has 170063 (86.7%) missing valuesMissing
ass_unlock_at has 171364 (87.4%) missing valuesMissing
ass_lock_at has 170145 (86.8%) missing valuesMissing
points_possible has 170063 (86.7%) missing valuesMissing
ass_name_sub has 171918 (87.7%) missing valuesMissing
sub_uuid has 171918 (87.7%) missing valuesMissing
score has 172248 (87.8%) missing valuesMissing
submission_type has 171918 (87.7%) missing valuesMissing
s_submitted_at has 171918 (87.7%) missing valuesMissing
s_graded_at has 172247 (87.8%) missing valuesMissing
s_created_at has 171918 (87.7%) missing valuesMissing
particion has 3002 (1.5%) zerosZeros

Reproduction

Analysis started2024-08-13 11:31:40.943854
Analysis finished2024-08-13 11:31:47.915126
Duration6.97 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

particion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.663891
Minimum0
Maximum59
Zeros3002
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:48.012049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median31
Q346
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.625074
Coefficient of variation (CV)0.57478269
Kurtosis-1.2272059
Mean30.663891
Median Absolute Deviation (MAD)15
Skewness-0.054307488
Sum6013925
Variance310.64322
MonotonicityNot monotonic
2024-08-13T08:31:48.155569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 7711
 
3.9%
55 4979
 
2.5%
54 3711
 
1.9%
56 3589
 
1.8%
44 3376
 
1.7%
30 3373
 
1.7%
21 3329
 
1.7%
43 3314
 
1.7%
48 3309
 
1.7%
29 3246
 
1.7%
Other values (50) 156187
79.6%
ValueCountFrequency (%)
0 3002
1.5%
1 3034
1.5%
2 3141
1.6%
3 3055
1.6%
4 3068
1.6%
5 3052
1.6%
6 3039
1.5%
7 3046
1.6%
8 3020
1.5%
9 3056
1.6%
ValueCountFrequency (%)
59 2996
 
1.5%
58 2996
 
1.5%
57 7711
3.9%
56 3589
1.8%
55 4979
2.5%
54 3711
1.9%
53 3078
 
1.6%
52 3133
1.6%
51 3209
1.6%
50 3180
1.6%

periodo
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1-2022
187854 
01-2022
 
7730
2-2022
 
540

Length

Max length7
Median length6
Mean length6.0394138
Min length6

Characters and Unicode

Total characters1184474
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-2022
2nd row1-2022
3rd row1-2022
4th row1-2022
5th row1-2022

Common Values

ValueCountFrequency (%)
1-2022 187854
95.8%
01-2022 7730
 
3.9%
2-2022 540
 
0.3%

Length

2024-08-13T08:31:48.289753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T08:31:48.402898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1-2022 187854
95.8%
01-2022 7730
 
3.9%
2-2022 540
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 588912
49.7%
0 203854
 
17.2%
- 196124
 
16.6%
1 195584
 
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1184474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 588912
49.7%
0 203854
 
17.2%
- 196124
 
16.6%
1 195584
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1184474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 588912
49.7%
0 203854
 
17.2%
- 196124
 
16.6%
1 195584
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1184474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 588912
49.7%
0 203854
 
17.2%
- 196124
 
16.6%
1 195584
 
16.5%

nota_final_materia
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5467
Minimum0
Maximum10
Zeros318
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:48.502043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7322749
Coefficient of variation (CV)0.22954072
Kurtosis0.7335068
Mean7.5467
Median Absolute Deviation (MAD)1
Skewness-0.78725705
Sum1480089
Variance3.0007764
MonotonicityNot monotonic
2024-08-13T08:31:48.613371image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 46956
23.9%
7 39423
20.1%
9 38849
19.8%
6 25104
12.8%
10 23644
12.1%
5 11592
 
5.9%
4 4519
 
2.3%
3 4301
 
2.2%
2 1418
 
0.7%
0 318
 
0.2%
ValueCountFrequency (%)
0 318
 
0.2%
2 1418
 
0.7%
3 4301
 
2.2%
4 4519
 
2.3%
5 11592
 
5.9%
6 25104
12.8%
7 39423
20.1%
8 46956
23.9%
9 38849
19.8%
10 23644
12.1%
ValueCountFrequency (%)
10 23644
12.1%
9 38849
19.8%
8 46956
23.9%
7 39423
20.1%
6 25104
12.8%
5 11592
 
5.9%
4 4519
 
2.3%
3 4301
 
2.2%
2 1418
 
0.7%
0 318
 
0.2%

fecha_mesa_epoch
Date

MISSING 

Distinct125
Distinct (%)1.8%
Missing189114
Missing (%)96.4%
Memory size1.5 MiB
Minimum2022-01-30 21:00:00-03:00
Maximum2022-07-22 21:00:00-03:00
2024-08-13T08:31:48.733482image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:48.871775image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

nombre_examen
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.1%
Missing189114
Missing (%)96.4%
Memory size1.5 MiB
SEGUNDO PARCIAL(20)
3213 
PRIMER PARCIAL(20)
3067 
INTEGRADOR(30)
356 
RECUPERATORIO PRIMER PARCIAL(20)
 
242
RECUPERATORIO SEGUNDO PARCIAL(20)
 
132

Length

Max length33
Median length32
Mean length19.02097
Min length14

Characters and Unicode

Total characters133337
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIMER PARCIAL(20)
2nd rowSEGUNDO PARCIAL(20)
3rd rowPRIMER PARCIAL(20)
4th rowSEGUNDO PARCIAL(20)
5th rowSEGUNDO PARCIAL(20)

Common Values

ValueCountFrequency (%)
SEGUNDO PARCIAL(20) 3213
 
1.6%
PRIMER PARCIAL(20) 3067
 
1.6%
INTEGRADOR(30) 356
 
0.2%
RECUPERATORIO PRIMER PARCIAL(20) 242
 
0.1%
RECUPERATORIO SEGUNDO PARCIAL(20) 132
 
0.1%
(Missing) 189114
96.4%

Length

2024-08-13T08:31:49.019493image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T08:31:49.139052image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
parcial(20 6654
47.4%
segundo 3345
23.8%
primer 3309
23.6%
recuperatorio 374
 
2.7%
integrador(30 356
 
2.5%

Most occurring characters

ValueCountFrequency (%)
R 15106
 
11.3%
A 14038
 
10.5%
I 10693
 
8.0%
P 10337
 
7.8%
E 7758
 
5.8%
7028
 
5.3%
C 7028
 
5.3%
) 7010
 
5.3%
( 7010
 
5.3%
0 7010
 
5.3%
Other values (11) 40319
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 15106
 
11.3%
A 14038
 
10.5%
I 10693
 
8.0%
P 10337
 
7.8%
E 7758
 
5.8%
7028
 
5.3%
C 7028
 
5.3%
) 7010
 
5.3%
( 7010
 
5.3%
0 7010
 
5.3%
Other values (11) 40319
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 15106
 
11.3%
A 14038
 
10.5%
I 10693
 
8.0%
P 10337
 
7.8%
E 7758
 
5.8%
7028
 
5.3%
C 7028
 
5.3%
) 7010
 
5.3%
( 7010
 
5.3%
0 7010
 
5.3%
Other values (11) 40319
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 15106
 
11.3%
A 14038
 
10.5%
I 10693
 
8.0%
P 10337
 
7.8%
E 7758
 
5.8%
7028
 
5.3%
C 7028
 
5.3%
) 7010
 
5.3%
( 7010
 
5.3%
0 7010
 
5.3%
Other values (11) 40319
30.2%

nota_parcial
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.2%
Missing189114
Missing (%)96.4%
Infinite0
Infinite (%)0.0%
Mean7.1714693
Minimum0
Maximum10
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:49.250176image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median7
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8232614
Coefficient of variation (CV)0.25423819
Kurtosis-0.3205849
Mean7.1714693
Median Absolute Deviation (MAD)1
Skewness-0.40054443
Sum50272
Variance3.3242822
MonotonicityNot monotonic
2024-08-13T08:31:49.367064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 1353
 
0.7%
8 1323
 
0.7%
9 1231
 
0.6%
6 1086
 
0.6%
5 789
 
0.4%
10 670
 
0.3%
4 368
 
0.2%
3 143
 
0.1%
2 36
 
< 0.1%
0 7
 
< 0.1%
(Missing) 189114
96.4%
ValueCountFrequency (%)
0 7
 
< 0.1%
1 4
 
< 0.1%
2 36
 
< 0.1%
3 143
 
0.1%
4 368
 
0.2%
5 789
0.4%
6 1086
0.6%
7 1353
0.7%
8 1323
0.7%
9 1231
0.6%
ValueCountFrequency (%)
10 670
0.3%
9 1231
0.6%
8 1323
0.7%
7 1353
0.7%
6 1086
0.6%
5 789
0.4%
4 368
 
0.2%
3 143
 
0.1%
2 36
 
< 0.1%
1 4
 
< 0.1%

assignment_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4239
Distinct (%)16.3%
Missing170063
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean204405.74
Minimum191573
Maximum228404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:49.498487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum191573
5-th percentile194619
Q1197229
median206604
Q3209619
95-th percentile212050
Maximum228404
Range36831
Interquartile range (IQR)12390

Descriptive statistics

Standard deviation6486.5143
Coefficient of variation (CV)0.031733522
Kurtosis-1.1964798
Mean204405.74
Median Absolute Deviation (MAD)4562
Skewness-0.29895996
Sum5.3270181 × 109
Variance42074868
MonotonicityNot monotonic
2024-08-13T08:31:49.649277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209612 203
 
0.1%
209297 195
 
0.1%
209433 195
 
0.1%
209334 195
 
0.1%
209257 195
 
0.1%
206612 190
 
0.1%
206615 185
 
0.1%
209606 182
 
0.1%
206614 179
 
0.1%
206613 172
 
0.1%
Other values (4229) 24170
 
12.3%
(Missing) 170063
86.7%
ValueCountFrequency (%)
191573 1
 
< 0.1%
191628 3
< 0.1%
191641 4
< 0.1%
191645 1
 
< 0.1%
191652 4
< 0.1%
191661 4
< 0.1%
191666 4
< 0.1%
191673 3
< 0.1%
191674 7
< 0.1%
191677 4
< 0.1%
ValueCountFrequency (%)
228404 5
< 0.1%
228403 5
< 0.1%
228402 5
< 0.1%
228400 5
< 0.1%
227980 1
 
< 0.1%
227979 1
 
< 0.1%
227978 1
 
< 0.1%
227977 1
 
< 0.1%
226341 1
 
< 0.1%
226340 1
 
< 0.1%

ass_name
Text

MISSING 

Distinct51
Distinct (%)0.2%
Missing170063
Missing (%)86.7%
Memory size1.5 MiB
2024-08-13T08:31:49.830924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length77
Median length72
Mean length31.850313
Min length17

Characters and Unicode

Total characters830051
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowActividad Práctica Integradora 1 [API1]
2nd rowTrabajo Práctico 1 [TP1]
3rd rowTrabajo Práctico 1 [TP1]
4th rowActividad Práctica Integradora 2 [API2]
5th rowTrabajo Práctico 2 [TP2]
ValueCountFrequency (%)
trabajo 13066
11.0%
práctico 13066
11.0%
actividad 12769
10.7%
práctica 12566
10.6%
integradora 12566
10.6%
4 6603
 
5.6%
3 6396
 
5.4%
2 6343
 
5.3%
1 6342
 
5.3%
tp2 3362
 
2.8%
Other values (44) 25851
21.7%
2024-08-13T08:31:50.120023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93014
 
11.2%
a 78387
 
9.4%
c 65379
 
7.9%
r 64123
 
7.7%
i 54103
 
6.5%
t 52301
 
6.3%
P 49776
 
6.0%
o 40081
 
4.8%
d 38209
 
4.6%
] 25935
 
3.1%
Other values (50) 268743
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 830051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
93014
 
11.2%
a 78387
 
9.4%
c 65379
 
7.9%
r 64123
 
7.7%
i 54103
 
6.5%
t 52301
 
6.3%
P 49776
 
6.0%
o 40081
 
4.8%
d 38209
 
4.6%
] 25935
 
3.1%
Other values (50) 268743
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 830051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
93014
 
11.2%
a 78387
 
9.4%
c 65379
 
7.9%
r 64123
 
7.7%
i 54103
 
6.5%
t 52301
 
6.3%
P 49776
 
6.0%
o 40081
 
4.8%
d 38209
 
4.6%
] 25935
 
3.1%
Other values (50) 268743
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 830051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
93014
 
11.2%
a 78387
 
9.4%
c 65379
 
7.9%
r 64123
 
7.7%
i 54103
 
6.5%
t 52301
 
6.3%
P 49776
 
6.0%
o 40081
 
4.8%
d 38209
 
4.6%
] 25935
 
3.1%
Other values (50) 268743
32.4%

ass_created_at
Date

MISSING 

Distinct1466
Distinct (%)5.6%
Missing170063
Missing (%)86.7%
Memory size1.5 MiB
Minimum2022-01-24 18:54:42-03:00
Maximum2022-07-22 15:00:35-03:00
2024-08-13T08:31:50.242535image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:50.386685image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ass_due_at
Date

MISSING 

Distinct48
Distinct (%)0.2%
Missing170063
Missing (%)86.7%
Memory size1.5 MiB
Minimum2022-02-09 23:59:59-03:00
Maximum2022-11-13 23:59:59-03:00
2024-08-13T08:31:50.529409image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:50.671481image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

ass_unlock_at
Date

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing171364
Missing (%)87.4%
Memory size1.5 MiB
Minimum2022-01-31 00:00:00-03:00
Maximum2022-08-08 00:00:00-03:00
2024-08-13T08:31:50.773554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:50.960409image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

ass_lock_at
Date

MISSING 

Distinct44
Distinct (%)0.2%
Missing170145
Missing (%)86.8%
Memory size1.5 MiB
Minimum2022-02-09 23:59:59-03:00
Maximum2022-11-13 23:59:59-03:00
2024-08-13T08:31:51.086272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:51.223632image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

points_possible
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing170063
Missing (%)86.7%
Memory size1.5 MiB
100.0
25954 
0.0
 
97
10.0
 
10

Length

Max length5
Median length5
Mean length4.9921722
Min length3

Characters and Unicode

Total characters130101
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 25954
 
13.2%
0.0 97
 
< 0.1%
10.0 10
 
< 0.1%
(Missing) 170063
86.7%

Length

2024-08-13T08:31:51.361308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T08:31:51.471287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
100.0 25954
99.6%
0.0 97
 
0.4%
10.0 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 78076
60.0%
. 26061
 
20.0%
1 25964
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 78076
60.0%
. 26061
 
20.0%
1 25964
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 78076
60.0%
. 26061
 
20.0%
1 25964
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 78076
60.0%
. 26061
 
20.0%
1 25964
 
20.0%

ass_name_sub
Text

MISSING 

Distinct91
Distinct (%)0.4%
Missing171918
Missing (%)87.7%
Memory size1.5 MiB
2024-08-13T08:31:51.618459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length85
Median length77
Mean length32.117781
Min length11

Characters and Unicode

Total characters777443
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowActividad Práctica Integradora 1 [API1]
2nd rowTrabajo Práctico 1 [TP1]
3rd rowActividad Práctica Integradora 2 [API2]
4th rowActividad Práctica Integradora 4 [API4]
5th rowTrabajo Práctico 2 [TP2]
ValueCountFrequency (%)
trabajo 12248
10.8%
práctico 12248
10.8%
actividad 11025
 
9.7%
práctica 10862
 
9.6%
integradora 10362
 
9.2%
3 6049
 
5.3%
1 5866
 
5.2%
2 5662
 
5.0%
4 5451
 
4.8%
tp2 3006
 
2.7%
Other values (64) 30307
26.8%
2024-08-13T08:31:51.923016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89236
 
11.5%
a 73498
 
9.5%
c 60571
 
7.8%
r 57451
 
7.4%
i 50255
 
6.5%
t 47787
 
6.1%
P 44467
 
5.7%
o 38259
 
4.9%
d 34664
 
4.5%
T 24632
 
3.2%
Other values (48) 256623
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 777443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
89236
 
11.5%
a 73498
 
9.5%
c 60571
 
7.8%
r 57451
 
7.4%
i 50255
 
6.5%
t 47787
 
6.1%
P 44467
 
5.7%
o 38259
 
4.9%
d 34664
 
4.5%
T 24632
 
3.2%
Other values (48) 256623
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 777443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
89236
 
11.5%
a 73498
 
9.5%
c 60571
 
7.8%
r 57451
 
7.4%
i 50255
 
6.5%
t 47787
 
6.1%
P 44467
 
5.7%
o 38259
 
4.9%
d 34664
 
4.5%
T 24632
 
3.2%
Other values (48) 256623
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 777443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
89236
 
11.5%
a 73498
 
9.5%
c 60571
 
7.8%
r 57451
 
7.4%
i 50255
 
6.5%
t 47787
 
6.1%
P 44467
 
5.7%
o 38259
 
4.9%
d 34664
 
4.5%
T 24632
 
3.2%
Other values (48) 256623
33.0%

sub_uuid
Text

MISSING 

Distinct21938
Distinct (%)90.6%
Missing171918
Missing (%)87.7%
Memory size1.5 MiB
2024-08-13T08:31:52.127520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters871416
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20916 ?
Unique (%)86.4%

Sample

1st rowef6fc92b-98da-40de-b440-1990379ee5e1
2nd row8891a347-bd41-4e5b-8c4b-cfea97c52985
3rd row3de31206-90b2-45ee-b40f-f45ee35b3384
4th row7fdbb439-8c7c-4524-ace1-5ac9f61961bc
5th row3834fa78-abf7-4ef3-87f5-76315ce1b71e
ValueCountFrequency (%)
02736ae3-000d-488b-8afa-38e7a92862ee 16
 
0.1%
e1f7cafd-27c7-4424-b9c7-fd1f92e6324f 16
 
0.1%
5c5e98f0-ea0c-4bc8-bc61-d26fcbe59991 8
 
< 0.1%
2671ee78-8053-47b4-bc99-fe97829de633 8
 
< 0.1%
4e66c39a-a64f-423f-8e98-863d014dbfe0 8
 
< 0.1%
561ec816-8ce9-4178-b98c-db9a7f37357b 8
 
< 0.1%
cf456655-ae71-45b2-aeaa-26274c74877c 8
 
< 0.1%
e448c2e2-4cbe-418a-8d97-91cfc3208a51 8
 
< 0.1%
9b0ec444-7392-45b1-9971-921db2e229d8 8
 
< 0.1%
2ae6cdac-1762-49fe-aae5-aed5f9caa8cb 8
 
< 0.1%
Other values (21928) 24110
99.6%
2024-08-13T08:31:52.456311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 96824
 
11.1%
4 69514
 
8.0%
a 51932
 
6.0%
b 51574
 
5.9%
8 51259
 
5.9%
9 51251
 
5.9%
7 45883
 
5.3%
0 45603
 
5.2%
1 45544
 
5.2%
5 45466
 
5.2%
Other values (7) 316566
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 871416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 96824
 
11.1%
4 69514
 
8.0%
a 51932
 
6.0%
b 51574
 
5.9%
8 51259
 
5.9%
9 51251
 
5.9%
7 45883
 
5.3%
0 45603
 
5.2%
1 45544
 
5.2%
5 45466
 
5.2%
Other values (7) 316566
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 871416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 96824
 
11.1%
4 69514
 
8.0%
a 51932
 
6.0%
b 51574
 
5.9%
8 51259
 
5.9%
9 51251
 
5.9%
7 45883
 
5.3%
0 45603
 
5.2%
1 45544
 
5.2%
5 45466
 
5.2%
Other values (7) 316566
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 871416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 96824
 
11.1%
4 69514
 
8.0%
a 51932
 
6.0%
b 51574
 
5.9%
8 51259
 
5.9%
9 51251
 
5.9%
7 45883
 
5.3%
0 45603
 
5.2%
1 45544
 
5.2%
5 45466
 
5.2%
Other values (7) 316566
36.3%

score
Real number (ℝ)

MISSING 

Distinct591
Distinct (%)2.5%
Missing172248
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean84.159968
Minimum0
Maximum100
Zeros238
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:52.597186image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q175
median88.333333
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.254782
Coefficient of variation (CV)0.21690576
Kurtosis4.3185999
Mean84.159968
Median Absolute Deviation (MAD)11.666667
Skewness-1.7285781
Sum2009403.4
Variance333.23705
MonotonicityNot monotonic
2024-08-13T08:31:52.741143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 8688
 
4.4%
80 1451
 
0.7%
90 1431
 
0.7%
70 1110
 
0.6%
75 945
 
0.5%
85 879
 
0.4%
95 617
 
0.3%
60 446
 
0.2%
87.5 382
 
0.2%
65 343
 
0.2%
Other values (581) 7584
 
3.9%
(Missing) 172248
87.8%
ValueCountFrequency (%)
0 238
0.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
5.3 1
 
< 0.1%
5.6 2
 
< 0.1%
6.25 1
 
< 0.1%
6.3 1
 
< 0.1%
7.1 1
 
< 0.1%
ValueCountFrequency (%)
100 8688
4.4%
98.75 15
 
< 0.1%
98.33333333 19
 
< 0.1%
98 6
 
< 0.1%
97.78 1
 
< 0.1%
97.5 53
 
< 0.1%
97.08333333 5
 
< 0.1%
97 5
 
< 0.1%
96.66666667 4
 
< 0.1%
96.66666667 69
 
< 0.1%

submission_type
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing171918
Missing (%)87.7%
Memory size1.5 MiB
external_tool
10191 
online_quiz
10020 
online_upload
3178 
basic_lti_launch
 
744
discussion_topic
 
51
Other values (2)
 
22

Length

Max length17
Median length13
Mean length12.274106
Min length11

Characters and Unicode

Total characters297107
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowexternal_tool
2nd rowonline_upload
3rd rowexternal_tool
4th rowexternal_tool
5th rowonline_upload

Common Values

ValueCountFrequency (%)
external_tool 10191
 
5.2%
online_quiz 10020
 
5.1%
online_upload 3178
 
1.6%
basic_lti_launch 744
 
0.4%
discussion_topic 51
 
< 0.1%
online_text_entry 20
 
< 0.1%
media_recording 2
 
< 0.1%
(Missing) 171918
87.7%

Length

2024-08-13T08:31:52.878169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T08:31:52.997449image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
external_tool 10191
42.1%
online_quiz 10020
41.4%
online_upload 3178
 
13.1%
basic_lti_launch 744
 
3.1%
discussion_topic 51
 
0.2%
online_text_entry 20
 
0.1%
media_recording 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 38266
12.9%
n 37444
12.6%
o 36882
12.4%
e 33644
11.3%
_ 24970
8.4%
i 24883
8.4%
t 21237
7.1%
a 14859
 
5.0%
u 13993
 
4.7%
r 10215
 
3.4%
Other values (12) 40714
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 38266
12.9%
n 37444
12.6%
o 36882
12.4%
e 33644
11.3%
_ 24970
8.4%
i 24883
8.4%
t 21237
7.1%
a 14859
 
5.0%
u 13993
 
4.7%
r 10215
 
3.4%
Other values (12) 40714
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 38266
12.9%
n 37444
12.6%
o 36882
12.4%
e 33644
11.3%
_ 24970
8.4%
i 24883
8.4%
t 21237
7.1%
a 14859
 
5.0%
u 13993
 
4.7%
r 10215
 
3.4%
Other values (12) 40714
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 38266
12.9%
n 37444
12.6%
o 36882
12.4%
e 33644
11.3%
_ 24970
8.4%
i 24883
8.4%
t 21237
7.1%
a 14859
 
5.0%
u 13993
 
4.7%
r 10215
 
3.4%
Other values (12) 40714
13.7%

s_submitted_at
Date

MISSING 

Distinct21812
Distinct (%)90.1%
Missing171918
Missing (%)87.7%
Memory size1.5 MiB
Minimum2022-01-28 13:52:05-03:00
Maximum2022-10-02 23:57:51-03:00
2024-08-13T08:31:53.133861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:53.272923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

s_graded_at
Date

MISSING 

Distinct21577
Distinct (%)90.4%
Missing172247
Missing (%)87.8%
Memory size1.5 MiB
Minimum2022-01-28 13:52:05-03:00
Maximum2022-10-18 23:52:01-03:00
2024-08-13T08:31:53.403580image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:53.544111image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

s_created_at
Date

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing171918
Missing (%)87.7%
Memory size1.5 MiB
Minimum2022-08-01 19:27:07-03:00
Maximum2022-10-17 12:21:56-03:00
2024-08-13T08:31:53.651477image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:53.754860image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
Distinct600
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:53.950072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7060464
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13df535e-065c-4593-98ea-5b1e29015b7d
2nd row13df535e-065c-4593-98ea-5b1e29015b7d
3rd row13df535e-065c-4593-98ea-5b1e29015b7d
4th row13df535e-065c-4593-98ea-5b1e29015b7d
5th row13df535e-065c-4593-98ea-5b1e29015b7d
ValueCountFrequency (%)
518e8c5f-6632-450b-bcee-518807ff2e9f 1017
 
0.5%
f2dbd20e-13f8-4d5c-b4ee-70b5ed837d5a 824
 
0.4%
ded91bdd-7998-4502-b2cb-9b0081eb3464 824
 
0.4%
0f58fa3f-0015-4064-99d9-ad9cb96b8fe6 814
 
0.4%
21056e82-1fe6-45a8-a059-a5a6387619c9 799
 
0.4%
fd3c4db8-cdfe-45e6-84c5-11e4ef5f3926 753
 
0.4%
6f171fde-7fb3-48e3-b5c0-e89c23631f1a 751
 
0.4%
e536d715-fb7a-4e0b-8ac8-bb48a4e39c49 744
 
0.4%
e854aa87-9ce5-4b8b-9399-5e201cbd4c29 714
 
0.4%
8caa4b71-4a42-4ad9-ab9f-238fcf328092 701
 
0.4%
Other values (590) 188183
96.0%
2024-08-13T08:31:54.350528image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 784496
 
11.1%
4 565460
 
8.0%
8 428708
 
6.1%
9 424687
 
6.0%
b 414567
 
5.9%
a 407145
 
5.8%
5 396733
 
5.6%
e 381774
 
5.4%
6 379974
 
5.4%
c 369055
 
5.2%
Other values (7) 2507865
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 565460
 
8.0%
8 428708
 
6.1%
9 424687
 
6.0%
b 414567
 
5.9%
a 407145
 
5.8%
5 396733
 
5.6%
e 381774
 
5.4%
6 379974
 
5.4%
c 369055
 
5.2%
Other values (7) 2507865
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 565460
 
8.0%
8 428708
 
6.1%
9 424687
 
6.0%
b 414567
 
5.9%
a 407145
 
5.8%
5 396733
 
5.6%
e 381774
 
5.4%
6 379974
 
5.4%
c 369055
 
5.2%
Other values (7) 2507865
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 565460
 
8.0%
8 428708
 
6.1%
9 424687
 
6.0%
b 414567
 
5.9%
a 407145
 
5.8%
5 396733
 
5.6%
e 381774
 
5.4%
6 379974
 
5.4%
c 369055
 
5.2%
Other values (7) 2507865
35.5%
Distinct582
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:54.578683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7060464
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row09614210-fce2-48bc-93e3-bc4bd441fe00
2nd row09614210-fce2-48bc-93e3-bc4bd441fe00
3rd row09614210-fce2-48bc-93e3-bc4bd441fe00
4th row09614210-fce2-48bc-93e3-bc4bd441fe00
5th row09614210-fce2-48bc-93e3-bc4bd441fe00
ValueCountFrequency (%)
2b274b15-83bb-4629-941e-fdc573af4e0b 11439
 
5.8%
c78af3b4-4574-4a82-8c41-45195ce43384 10341
 
5.3%
12bb6bfa-8b01-46a8-8cc2-e0e84785fa1e 4555
 
2.3%
0034afe6-e996-4c26-b0b9-24dbb9535465 3997
 
2.0%
68005d22-90dc-42f6-b90a-409714481cbc 3537
 
1.8%
06dd5f32-293c-442e-b602-08107eb61ea8 3332
 
1.7%
41fc218f-a64e-4633-9efd-62ad28943f04 2825
 
1.4%
e14f5a9d-992e-4d5f-b9e2-a0c723b4219b 2823
 
1.4%
9fb5bf9b-b5e3-40ef-b69e-8681fa52cb8d 2636
 
1.3%
d17e238c-c4c6-40d7-8085-801268d6d723 2630
 
1.3%
Other values (572) 148009
75.5%
2024-08-13T08:31:54.904218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 784496
 
11.1%
4 614842
 
8.7%
b 451116
 
6.4%
8 426140
 
6.0%
9 407611
 
5.8%
2 399891
 
5.7%
a 379756
 
5.4%
3 378527
 
5.4%
c 371444
 
5.3%
e 366796
 
5.2%
Other values (7) 2479845
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 614842
 
8.7%
b 451116
 
6.4%
8 426140
 
6.0%
9 407611
 
5.8%
2 399891
 
5.7%
a 379756
 
5.4%
3 378527
 
5.4%
c 371444
 
5.3%
e 366796
 
5.2%
Other values (7) 2479845
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 614842
 
8.7%
b 451116
 
6.4%
8 426140
 
6.0%
9 407611
 
5.8%
2 399891
 
5.7%
a 379756
 
5.4%
3 378527
 
5.4%
c 371444
 
5.3%
e 366796
 
5.2%
Other values (7) 2479845
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7060464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 784496
 
11.1%
4 614842
 
8.7%
b 451116
 
6.4%
8 426140
 
6.0%
9 407611
 
5.8%
2 399891
 
5.7%
a 379756
 
5.4%
3 378527
 
5.4%
c 371444
 
5.3%
e 366796
 
5.2%
Other values (7) 2479845
35.1%
Distinct561
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:55.119687image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length62
Median length46
Mean length33.475153
Min length19

Characters and Unicode

Total characters6565281
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProgressive homogeneous structure
2nd rowProgressive homogeneous structure
3rd rowProgressive homogeneous structure
4th rowProgressive homogeneous structure
5th rowProgressive homogeneous structure
ValueCountFrequency (%)
ability 14129
 
2.3%
leadingedge 12748
 
2.0%
non-volatile 12574
 
2.0%
intuitive 11630
 
1.9%
help-desk 11391
 
1.8%
object-based 10523
 
1.7%
radical 8637
 
1.4%
face-to-face 8219
 
1.3%
customizable 7289
 
1.2%
secured 7094
 
1.1%
Other values (303) 521644
83.3%
2024-08-13T08:31:55.464148image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 803028
 
12.2%
t 524240
 
8.0%
i 494332
 
7.5%
a 459870
 
7.0%
429754
 
6.5%
n 400188
 
6.1%
r 373787
 
5.7%
o 356666
 
5.4%
l 301868
 
4.6%
d 284837
 
4.3%
Other values (44) 2136711
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6565281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 803028
 
12.2%
t 524240
 
8.0%
i 494332
 
7.5%
a 459870
 
7.0%
429754
 
6.5%
n 400188
 
6.1%
r 373787
 
5.7%
o 356666
 
5.4%
l 301868
 
4.6%
d 284837
 
4.3%
Other values (44) 2136711
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6565281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 803028
 
12.2%
t 524240
 
8.0%
i 494332
 
7.5%
a 459870
 
7.0%
429754
 
6.5%
n 400188
 
6.1%
r 373787
 
5.7%
o 356666
 
5.4%
l 301868
 
4.6%
d 284837
 
4.3%
Other values (44) 2136711
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6565281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 803028
 
12.2%
t 524240
 
8.0%
i 494332
 
7.5%
a 459870
 
7.0%
429754
 
6.5%
n 400188
 
6.1%
r 373787
 
5.7%
o 356666
 
5.4%
l 301868
 
4.6%
d 284837
 
4.3%
Other values (44) 2136711
32.5%

legajo
Text

Distinct601
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-13T08:31:55.695579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1765116
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row834066QFF
2nd row834066QFF
3rd row834066QFF
4th row834066QFF
5th row834066QFF
ValueCountFrequency (%)
624097jcn 1017
 
0.5%
694137gri 824
 
0.4%
614851wsa 824
 
0.4%
303555jen 814
 
0.4%
220932bsh 799
 
0.4%
108608onk 753
 
0.4%
372740jai 751
 
0.4%
210665agt 744
 
0.4%
262705gps 714
 
0.4%
329231zku 701
 
0.4%
Other values (591) 188183
96.0%
2024-08-13T08:31:56.043064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 131344
 
7.4%
4 129245
 
7.3%
3 124418
 
7.0%
7 119379
 
6.8%
2 117948
 
6.7%
1 116943
 
6.6%
0 116499
 
6.6%
5 110387
 
6.3%
8 107673
 
6.1%
9 102908
 
5.8%
Other values (26) 588372
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1765116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 131344
 
7.4%
4 129245
 
7.3%
3 124418
 
7.0%
7 119379
 
6.8%
2 117948
 
6.7%
1 116943
 
6.6%
0 116499
 
6.6%
5 110387
 
6.3%
8 107673
 
6.1%
9 102908
 
5.8%
Other values (26) 588372
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1765116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 131344
 
7.4%
4 129245
 
7.3%
3 124418
 
7.0%
7 119379
 
6.8%
2 117948
 
6.7%
1 116943
 
6.6%
0 116499
 
6.6%
5 110387
 
6.3%
8 107673
 
6.1%
9 102908
 
5.8%
Other values (26) 588372
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1765116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 131344
 
7.4%
4 129245
 
7.3%
3 124418
 
7.0%
7 119379
 
6.8%
2 117948
 
6.7%
1 116943
 
6.6%
0 116499
 
6.6%
5 110387
 
6.3%
8 107673
 
6.1%
9 102908
 
5.8%
Other values (26) 588372
33.3%

Interactions

2024-08-13T08:31:45.799969image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:43.756143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.293933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.825157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.299686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.894554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:43.882220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.450848image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.924563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.393889image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.984460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:43.994950image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.541490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.015950image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.491817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:46.085558image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.090802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.631296image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.104850image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.586393image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:46.188248image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.193199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:44.736249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.203259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-13T08:31:45.699325image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-13T08:31:56.136452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
assignment_idnombre_examennota_final_materianota_parcialparticionperiodopoints_possiblescoresubmission_type
assignment_id1.0000.218-0.034-0.0030.3180.8430.327-0.0200.296
nombre_examen0.2181.0000.1590.1120.5150.0000.0590.1510.142
nota_final_materia-0.0340.1591.0000.781-0.0070.0700.0260.1680.050
nota_parcial-0.0030.1120.7811.000-0.0740.0510.0000.1560.062
particion0.3180.515-0.007-0.0741.0000.0230.061-0.2330.231
periodo0.8430.0000.0700.0510.0231.0000.0010.0280.099
points_possible0.3270.0590.0260.0000.0610.0011.0000.0000.405
score-0.0200.1510.1680.156-0.2330.0280.0001.0000.208
submission_type0.2960.1420.0500.0620.2310.0990.4050.2081.000

Missing values

2024-08-13T08:31:46.383706image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T08:31:46.874533image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-13T08:31:47.536999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

particionperiodonota_final_materiafecha_mesa_epochnombre_examennota_parcialassignment_idass_nameass_created_atass_due_atass_unlock_atass_lock_atpoints_possibleass_name_subsub_uuidscoresubmission_types_submitted_ats_graded_ats_created_atuser_uuidcourse_uuidcourse_namelegajo
001-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
111-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
221-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
331-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
441-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
551-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
661-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
771-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
881-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
991-20229.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT13df535e-065c-4593-98ea-5b1e29015b7d09614210-fce2-48bc-93e3-bc4bd441fe00Progressive homogeneous structure834066QFF
particionperiodonota_final_materiafecha_mesa_epochnombre_examennota_parcialassignment_idass_nameass_created_atass_due_atass_unlock_atass_lock_atpoints_possibleass_name_subsub_uuidscoresubmission_types_submitted_ats_graded_ats_created_atuser_uuidcourse_uuidcourse_namelegajo
196114501-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196115511-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196116521-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196117531-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196118541-202210.0NaTNoneNaN205727.0Trabajo Práctico 4 [TP4]2022-03-16 13:26:15-03:002022-05-26 23:59:59-03:002022-03-21 00:00:00-03:002022-05-26 23:59:59-03:00100.0NoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196119551-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196120561-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196121571-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196122581-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX
196123591-202210.0NaTNoneNaNNaNNoneNaTNaTNaTNaTNaNNoneNoneNaNNoneNaTNaTNaT1f043fbc-2e51-4639-b99a-00e96f86968dd9cc0ef0-3282-4c10-b2c7-bc231a26ca6aCustomizable hybrid forecast398800TRX

Duplicate rows

Most frequently occurring

particionperiodonota_final_materiafecha_mesa_epochnombre_examennota_parcialassignment_idass_nameass_created_atass_due_atass_unlock_atass_lock_atpoints_possibleass_name_subsub_uuidscoresubmission_types_submitted_ats_graded_ats_created_atuser_uuidcourse_uuidcourse_namelegajo# duplicates
001-20223.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT1fefef88-4ce4-46eb-8e87-353edf9c5113a8344c00-e727-4d50-b628-21b07b784305Programmable multi-state circuit397430YMQ2
101-20223.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT2bc2fb30-95f8-42ff-b29e-8babf90640ce7bfb456b-0de2-4457-95a8-ddcc06ede020Expanded didactic secured line286422CWL2
201-20224.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT53e6f60f-188c-430d-b4c9-7452395ff17fd1ecb02f-5535-41ac-a0ee-9e620c3d84caConfigurable human-resource challenge869414PXB2
301-20225.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT46d5ce2d-79ed-4207-a5bd-87b63ffc1de80034afe6-e996-4c26-b0b9-24dbb9535465Stand-alone upward-trending secured line505929FOH2
401-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT3c0cc1c4-10d6-4136-b333-b4a040ff113e2dfe5582-1e1d-48d0-ae90-aea2b1ebea14Persevering coherent capacity255723BSQ2
501-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT6bd85c64-d0bf-44c1-9f0b-b42c0d98294c24ce2bde-18e6-4a2a-8297-9d6d8bf084bcVersatile 5thgeneration access942736XSX2
601-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT97f8c0d4-2c75-4bb5-a37e-3e6f4375e430c78af3b4-4574-4a82-8c41-45195ce43384Object-based leadingedge help-desk934519YSE2
701-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaT9f0d8e0e-a48d-4d75-9eae-71fbbff1df19a385c97d-ac69-4614-96a6-3974680abd9cDe-engineered disintermediate infrastructure099435OXF2
801-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaTb51254e4-b9e9-490a-b6d5-968c2acb8a94f3632565-3ab8-4b7f-9ced-097ce82eb1c5Customer-focused interactive success123064ZQJ2
901-20226.0NaTNaNNaNNaNNaNNaTNaTNaTNaTNaNNaNNaNNaNNaNNaTNaTNaTeb64b14f-02e2-48dd-a543-9c32176c41ffe06ea75d-ed55-4638-afe4-14dd94e2ceafSeamless scalable adapter075452VVF2